Key drivers of digital twin maintenance management for healthcare facilities in Nigeria Open Access

The technologies employed in the construction sector differ depending on the project phase (design, execution, operation, maintenance, and recovery), just like in other industries. Similarly, depending on the stage of the business, digital twins’ level of applicability and services will vary (Menegon and Isatto, 2023). Digital twins can help simulate the surroundings, the land, and nearby buildings during the design phase when physical parts have not yet been erected. This can be done to construct new facilities and repair or expand existing ones (Jiang et al., 2021). This information can assist in designing and developing a new project through simulations, conflict analysis, and scenario planning. Sites with intelligent systems connected to a DT can help manage and monitor real-time operations during construction. According to Jiang et al. (2021), building information modelling (BIM) and cyber-physical systems can be coupled in this step to create a DT of sections that have already been built, which helps keep track of personnel, equipment, and supplies. It should be emphasised that since a DT needs to be linked to a real-world environment, the virtual model does not consider components that have not yet been constructed. Because the physical component has been fully implemented during the operation and maintenance phase, a DT is more applicable and can mimic and monitor physically present systems. A DT of an existing facility can assist in disaster prevention and retrofitting efforts, analysis and diagnosis, decision-making, and asset management and monitoring (Jiang et al., 2021). Each application will need a unique model with varying authenticity and complexity. In their review of DT applications and research possibilities in the construction industry, Kan and Anumba (2019) determined that DT is essential for enhancing project lifecycle processes, particularly those that take place during the design, construction, operations and maintenance phases. This indicates that DT is significant in efficiently managing constructed facilities.

DT technology has developed from an information monitoring tool to a decision-making tool via digital simulation, IoT deployment, and connection (Warke et al., 2021). IoT, IIoT, cloud computing, virtual/augmented/mixed realities, data analytics, and artificial intelligence are examples of enabling technologies recognised for DT usage (Warke et al., 2021). Additionally, according to Opoku et al. (2021), DT can address issues plaguing the construction sector and boost productivity. Present construction processes are intended to be improved through the dynamic cyber-physical integration possible in digital twins. A DT can display an asset’s behaviour, spatiotemporal status, and geometry (Tao et al., 2018). DT solutions can minimise expenses and enhance the patient experience as hospitals fight to cut operating costs and stay competitive (Ross, 2016). Healthcare firms can benefit from using digital twins to optimise the workforce, workflows, hospitals, and other aspects of care delivery (El Saddik, 2018). For instance, DT’s improved predictive analytics by merging patient flow internally and projecting future spikes using external data can offset the effects of erroneous bed occupancy projections (Ross, 2016). To improve patient care, operational effectiveness, and financial success, healthcare providers should leverage the data from DT solutions to think more strategically about capacity and resources based on better forecasting. To increase productivity and save costs, digital twins can also be utilised to model personalised and intelligent medical devices and other equipment (Ross, 2016; El Saddik, 2018). To acquire data regarding the configuration and maintenance history of the equipment, DT uses data collected by IoT sensors implanted in the device. The healthcare sector has much to gain from adopting DT in managing constructed facilities.

The need to concentrate on the healthcare sector in this study is based on the United Nations Sustainable Development Goals (SDGs) 2030 Agenda. Specifically, the third and ninth SDG was selected to underpin this study within the healthcare sector. They are to ensure healthy lives and promote well-being for all at all ages; and to build resilient infrastructure, promote inclusive and sustainable industrialisation and foster innovation. A merger of these goals is critical to foster the need for adopting DT technology in the management of healthcare facilities. The healthcare sector in developing nations lacks clear policies and strategies for the adequate maintenance of hospital buildings and the performance of the facilities that create a good working environment; instead, they have chosen to concentrate on the main activity of providing clinical services in hospitals (Amos et al., 2020). As a result, structural inconsistencies and subpar service delivery plague the built environment and facilities of the healthcare sector in several developing nations, leading to unhygienic healthcare settings and related hospital-acquired diseases (Pasqualini Blass et al., 2016). Could these ascertained issues be caused by the absence of a more vigorous technological process that will support the maintenance activities of the healthcare sector? Considering how the construction sector is generally seen in underdeveloped nations, especially in Nigeria, maintenance works are still lagging in using technological processes to manage buildings efficiently and other facilities (Ebiloma et al., 2023). There is a low level of awareness of the benefits of digital technologies and Industry 4.0 tools in the management of constructed facilities in Nigeria (Ebiloma, 2024). According to preliminary studies in the Nigerian healthcare sector, only a few technologies, like Microsoft Access and Computerised Maintenance Management Software, are used for asset inventory, which is minimal in attaining efficient maintenance management (Ebiloma, 2024). Other existing technologies that have been used for efficient maintenance management of facilities in other climes would have been advanced for Nigeria’s public hospitals, but it has been iterated as part of the research problem that BIM, which is the most advanced technology for maintenance management, has its setbacks and inadequacies. Hence, the need for DT technology to manage healthcare facilities has become a priority. Considering the foregoing, it is deduced by this study that the lack of efficient and smart maintenance management can be handled by adopting DT technology in the Nigerian healthcare sector to manage hospital buildings adequately. There is a dearth of studies that have evaluated the benefits and outcomes of adopting DT for the management of constructed facilities in Nigeria and Africa to aid its adoption. Hence, it is necessary to explore and ascertain the result of adopting DT in managing hospital buildings in Nigeria, since it offers innumerable benefits in maintenance management. The study poses the following question: What are the factors that will motivate healthcare industry stakeholders to adopt DT in managing healthcare facilities? Given this, this study aims to determine the key drivers of digital twin maintenance management (DTMM) for healthcare facilities in Nigeria. The specific objectives are to identify the possible drivers of DTMM and validate the identified drivers of DTMM for healthcare facilities in Nigeria. The importance of this study is mainly to foster the adoption of DT in the management of healthcare facilities in Nigeria. In addition to helping to attain a robust level of routine maintenance activities that enable more efficient and effective use of staff resources in healthcare institutions, the study is significant from the perspective that when the DT is adopted through the knowledge of its benefits, impending problems will be detected before failure occurs resulting in fewer failures and users’ complaints.

Numerous benefits from the use of DT in the construction industry, particularly in the management of built facilities, have been outlined by Madni et al. (2019). Although its implementation could need a sizable up-front investment, the author claimed that it is cost-effective in the long term and offers advantages for performance monitoring. The following potential advantages could result from the effective application of the suggested DT platform for managing healthcare facilities, as advanced by Madubuike and Anumba (2023): monitoring the pressure and indoor air quality (IAQ) in real-time to avoid any negative impacts that could be brought on by any deviation from the recommended air quality; supplying facilities managers with the necessary pictures of the healthcare facilities and their equipment through the virtual prototyping component; early defect identification and alerts on the potential failure of a system or piece of medical apparatus, such as the MRI machine afflicted by inadequate IAQ, can avoid breakdown or malfunction; the ability to use sensor measurements of the air quality to continuously monitor the heating, ventilation, and air-conditioning (HVAC) system’s operation; the ability to track mobile medical equipment with the hospital and its precise location as shown on a 3D Revit model; the ability to cut costs that could be incurred by unanticipated equipment failure or unneeded scheduled equipment replacement; and scheduled maintenance reduces the cost of maintenance because it is also based on the equipment’s performance rather than calling in a maintenance crew (Madubuike and Anumba, 2023). A few of these variables from the authors’ review are considered in this study for a well-grounded scientific inquiry, as most are concerned about other hospital equipment apart from the actual building facility. A summary of the outcomes of DT applications, as examined by Opoku et al. (2022), is provided below. The authors’ thorough content research led to the discovery of fifty (50) outcomes of DT adoption in the construction sector. These potential possibilities were taken, categorised, and put together into four major groups of the result variables – concept-oriented, operational success, production, and preservation-driven variables. The operational success variables were selected to underpin this study since they relate to the maintenance phase of constructed facilities. The operational success factors had a close relationship with the maintenance management of constructed buildings; hence, it was necessary to consider them only.

Operational success factors connect the construction project users and themselves (Khajavi et al., 2019). At this phase, users value the project’s dependability and convenience. The construction project is operated by various stakeholders, which hinders data integration between those stakeholders and the project (Boje et al., 2020). Facilities and maintenance management are typically the focus of DT adoption at a project’s operation and maintenance stage (Khajavi et al., 2019; Sacks et al., 2020). Using DT, the communication between the various stakeholders might also be improved. Sixteen (16) different outcomes describe the operational success variables. This category includes maintaining occupant comfort, enhancing environmental monitoring, enhancing energy management, continuous asset monitoring, enhancing predictive maintenance, real-world asset management, automation and real-time control, better project operational performance, enhancing operational cost, and improving ergonomic exposure (Opoku et al., 2022). It is important to note that, according to Opoku et al. (2021), this category has been identified as one of the most important factors influencing the adoption of DTs in the construction industry. Improved environmental monitoring, improved energy management, continuous asset monitoring, and improved predictive maintenance are the key factors in this category (Ozturk, 2021; Porsani et al., 2021; Boje et al., 2020; Sepasgozar, 2021). In recent years, ensuring the comfort of building inhabitants has grown significantly in importance. To ensure the comfort of the building’s occupants, facilities managers are faced with the issue of making crucial decisions about the operation and maintenance of the property. According to Khajavi et al. (2019), the DT enhances informed decision-making by increasing the operational efficiency of the buildings based on data acquired in real-time. Implementing DTs in the construction sector will allow for monitoring various environmental characteristics, such as room and ambient temperatures, relative humidity, lux levels, and decibel levels within buildings, to enhance the environment. DT also allows building energy management systems to be intelligently optimised and automated. For instance, Agostinelli et al. (2021) created a DT-based real-time system for tracking building energy efficiency. According to the authors, the methodology could narrow the difference between the buildings’ simulated and actual energy performance. DT offers a fantastic and improved potential for building predictive maintenance. According to D'Addona et al. (2015), DT offers the framework for identifying and assessing proactive solutions for asset predictive maintenance. Additionally, DT improves predictive maintenance and guarantees that facility decisions are made with full knowledge (Opoku et al., 2021; Khajavi et al., 2019). Based on these review studies, the factors that relate to and fit with the aim of this study were carefully selected for empirical and scientific validation in the maintenance management of healthcare facilities in Nigeria.

Considering the foregoing, this study carefully selected seventeen (17) variables to evaluate the DTMM drivers for healthcare facilities in Nigeria. They include design information exchange among professionals, smart maintenance request system, better team collaboration among stakeholders, accelerated risk assessment, improved decision-making for maintenance processes, improved financial decision-making, enhanced traceability process of building failures, smart management of site activities, increased collection of real-time information on buildings, prediction of building component failures, maintenance cost reduction due to prediction before failures, increased reliability of building components, increased level of integration among building components, increased digitalisation of maintenance activities, increased information and drive for sustainability efforts, increased scheduled maintenance from predictions, and remote troubleshooting regardless of geographical location. The list of identified drivers is presented in Table 1. However, since the concept of DTMM is innovative in Nigeria, a group of experts was selected to validate these variables before its evaluation within the Nigerian healthcare sector.

The study evaluated the drivers for DTMM of healthcare facilities in Nigeria. The Nigerian healthcare sector was selected for this study since there was easy access to the needed information and the urgent need to transform the healthcare sector, especially in the post-COVID era. This study used a methodology predicated on a positivist philosophical framework. It used a mixed-method research design, combining quantitative and qualitative inquiry methods. Creswell (2014) noted that a mixed-method study supplies more thorough authentication for examining a research problem than any qualitative or quantitative approach. The pragmatic (exploratory sequential mixed method) approach was utilised to respond to the research question and achieve the aim. The structured and semi-structured interview (using an interview guide) is the qualitative research approach used in this study. This was facilitated by the Delphi method. The Delphi method is predicated on the idea that “pooled intelligence” enhances individual judgement and captures the consensus view of a group of experts (Shariff, 2015). The Delphi survey featured twenty-two (22) invited panellists, and eleven (11) remained active participants. A high level of academic and practical expertise in maintenance management using digital technologies in constructed facilities was considered while choosing the panel of experts; all the chosen specialists possessed these qualities. These were regarded as extremely important since professionals needed extensive knowledge of maintenance management and the application of digital technologies in constructed facilities. The eleven (11) panellists were deemed adequate based on recommendations from academics who have used the technique in prior studies (Chan et al., 2001; Hallowell and Gambatese, 2010; Aigbavboa, 2014; Ameyaw et al., 2016). The findings from this part were used to validate the literature review findings and improve the survey instrument (structured questionnaire). This methodology was selected for this study because the concept of digital twins is novel within the study area; there is a need to validate the variables using the mixed methods. The identified drivers from the literature review are shown in Table 1. Using the consensus process (agreement and disagreement), concerns relating to the inefficient maintenance management of hospital buildings in the study region (Nigeria) were resolved using the Delphi findings. After requesting questionnaire responses from the experts that made up the Delphi panel, an agreement was achieved on the ranked possibilities and influence of the factors. The major goal of the three-round iterative Delphi procedure was to get experts to agree on the identified drivers. The expert group was also urged to explain why they held different opinions. Microsoft Excel, a spreadsheet program in the Microsoft Office Suite, was used to assess the data from the Delphi study. A collection of descriptive statistics, including mean, standard deviations, and their associated derivatives, was produced by the analysis. A measure of the central tendency of all the responses by the expert panellists was used to ascertain consensus. The group median and the IQD were used in this investigation. For each response, the group median and IQD were computed. To reach a consensus, it was decided that the IQD should not deviate by more than one (1) unit from the group median for any of the Delphi panellists’ responses. This was deemed appropriate because both influence (probability) and impact were measured on a scale of 1–10 (Aigbavboa, 2014). The IQD was computed using the advised statistical method of the absolute difference value between the 75th (Q3 or upper quartile) and 25th (Q1 or lower quartile) percentiles. The deviation of all responses from the expert panel was calculated using the absolute median.

The quantitative data were obtained through a questionnaire survey from maintenance personnel and major stakeholders in the Nigerian healthcare sector. The questionnaire survey was developed based on the Delphi study findings and was chosen because it facilitates data collection from many respondents and makes the research quantifiable and objective (Tan, 2011). The questionnaire was divided into sections A and B. Section A comprised demographic data such as years of experience, educational qualifications, professional affiliation, and respondents’ roles. Section B contained the drivers for DTMM of constructed facilities; the variables were obtained from the review of related literature as advanced in other climes, and as validated from the Delphi study. Respondents were asked to indicate the extent to which they agree with the drivers for DTMM in the Nigerian healthcare sector. A five-point Likert scale was used, which ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). Maintenance professionals proficient in digital technologies, top management staff, and heads of units were purposively selected as respondents, and the questionnaire was administered to them. The maintenance professionals who formed the main and highest number of respondents were purposively selected from the tertiary and secondary healthcare facilities in Lagos and Abuja, Nigeria, since they are expected to be the most involved in managing constructed facilities. The top management staff and heads of units in the healthcare facilities were included as respondents because they are involved in the decision-making process to maintain healthcare facilities. From the 2,688 maintenance personnel, top management staff, and heads of units of public secondary and tertiary hospitals in the study area (Adedayo et al., 2015; Obubu et al., 2023), the sample size was chosen and approximated to be four hundred and fifty (450); this is quite above 15% of the population as recommended by previous authors outlined below. The number of people included in a sample is known as the sample size (Kadam and Bhalerao, 2010); it is chosen based on the cost of data collection and the requirement for statistical power (Kadam and Bhalerao, 2010; Aigbavboa, 2014). The target sample size of 450 respondents was set based on the need for statistical power and the percentage requirement, as Kadam and Bhalerao (2010) recommended. The questionnaire administration began with the respondents in the tertiary facilities before moving to the secondary facilities until the target was reached. This order was followed since the scope of activities, nature of facilities and managerial structure in the hospitals to adopt innovative technologies differ and descend from the tertiary to the secondary healthcare facilities.

A total of 442 completed questionnaires were received and were suitable for the analysis. Golzar et al. (2022) recommended that authors disperse the questionnaires at various times and locations to obtain a suitable cross-section of the target population; this was done in Lagos and Abuja hospitals. The data analysis used the Statistical Package for the Social Sciences (SPSS), version 29. Descriptive statistical measures, including frequency distributions, mean values, and standard deviations, were computed to provide a foundational understanding of the dataset, with rankings assigned to factors based on their respective scores. Subsequently, confirmatory factor analysis (CFA) was executed within the framework of structural equation modelling (SEM), providing a rigorous assessment of the factor structure and validating hypothesised relationships among the constructs, enhancing the robustness and reliability of the analytical outcomes. CFA rigorously tests and validates these structures using a separate subsample or cross-validation. Moreover, the reliability and internal consistency of the collected data were assessed based on Cronbach’s alpha threshold of 0.70. The average value for the variables was 0.889, as shown in Table 2, indicating excellent reliability and internal consistency of the collected data (Pallant, 2020). The flow chart for the study methodology is presented in Figure 1. SEM has emerged as a robust and versatile approach in model construction across multiple disciplines over recent decades (Yuan, 2005; Hooper et al., 2008). SEM surpasses traditional multivariate or univariate methods in comprehensiveness and adaptability, offering the ability to model complex relationships between both unobserved (latent) and observed (manifest) variables (Byrne, 2010; Aigbavboa, 2014). By achieving a well-fitted model, SEM validates specific causal relationships, providing enhanced capacity to control extraneous or confounding variables and account for measurement error, strengthening its causal inference capabilities (Narayanan, 2012). Due to these advantages, SEM was selected over alternative methods, such as ANOVA, multiple regression, or path analysis, for this study (Agumba, 2013; Aigbavboa, 2014). SEM typically requires substantial sample sizes and specialised software for analysis. SEM is currently the most inclusive statistical method used in scientific and social research; it supports all functions related to general linear modelling, including ANOVA, MANOVA and multiple regression (Agumba, 2013; Aigbavboa, 2014). Any study topic involving the direct or indirect observation of one or more dependent variables or one or more independent variables can theoretically be answered using the SEM. However, the main objective of SEM is to identify and validate a suggested causal process and/or model (Aigbavboa, 2014). The DTMM drivers’ model for hospital buildings in Nigeria’s healthcare system is being validated in this study. Byrne (2010) and Aigbavboa (2014:320) claim that SEM employs a confirmatory methodology when analysing a structural theory about a phenomenon. However, SEM concurrently estimates all model coefficients; therefore, it is possible to evaluate the strength and relevance of a link in the context of the entire posited model, according to Dion (2008, p. 365). Furthermore, SEM frequently distinguishes between real and error variance, suggesting that model parameters are typically computed while accounting for measurement error. This study used SEM, operationalised through EQS software (Version 6.4), to examine the drivers of DTMM within Nigeria’s healthcare infrastructure sector. Data processed via SPSS was transferred into EQS for CFA, facilitating the rigorous examination of hypothesised relationships.

A review of the relevance of DT technology in maintaining constructed facilities guided the identification of possible outcomes that could emerge within the Nigerian healthcare sector. An ordinal scale of one (1) to ten (10), with one (1) denoting low influence or no impact and ten (10) denoting great influence or extremely high impact, was used to assign the rating. As a result of the consensus reached using the accepted scale for assessing consensus in the Delphi study, the levels of influence and impact were then determined. The result of the study is shown in Table 3. The findings revealed that out of the 17 listed variables, each component had a very high impact (VHI: 9.00–10.00). Therefore, it was determined that all the variables are possible drivers of DTMM in the Nigerian healthcare sector. Additionally, the IQD results showed that a strong consensus was reached for the seventeen (17) items, as they received scores between 0.00 and 1.00. The fact that their individual standard deviations (σx) were at most 1 further demonstrated the uniformity of the experts’ responses. Additionally, when comparing the items’ respective mean scores, the enhanced traceability process of building failures ranked 1st out of the 17 variables, while the Increased digitalisation of maintenance activities was ranked 17th.

The Delphi survey sought to identify the possible outcomes that could emerge from the adoption of DT technology for the maintenance management of hospital buildings in Nigeria. From the analysis, the experts’ responses indicated that out of the 17 listed variables, all the items had a very high impact on determining DTMM. The factors that were foremost and with a very high impact are the enhanced traceability process of building failures, accelerated risk assessment, increased reliability of building components, increased level of integration among building components, and improved financial decision-making, all with higher mean values. This outcome supports the view advanced by Tao et al. (2019), Madni et al. (2019), Mohammadi et al. (2021), Opoku et al. (2022), and Madubuike and Anumba (2023). In conclusion, all the possible outcomes identified from existing literature that could emerge from adopting DT were found to be the drivers that will emerge from adopting digital twins for the maintenance management of buildings in the Nigerian healthcare sector. Moreover, though none of the factors had a low impact among the drivers of DT adoption for the maintenance management of hospital buildings: Enhanced traceability process of building failures, Accelerated risk assessment, Increased reliability of building components, Increased level of integration among building components, and Improved financial decision-making; were seen to be the foremost outcomes that will emerge from the adoption of DT technology for the maintenance management of hospital buildings in Nigeria. These findings from the Delphi survey, which was based on expert opinion, were validated using the questionnaire survey among maintenance professionals and key healthcare stakeholders in Nigeria.

After completing the questionnaire survey, 442 responses were collected for analysis in this study. The demographic characteristics of the respondents are broken down in Table 4. From the four hundred and forty-two (442) responses, the outcome on the respondents’ years of experience revealed that none of the respondents had experience between 1–5 years, 13.6% of them were experienced within 6–10 years, 36.2% were experienced within 11–15 years, 23.8% were experienced within 16–20 years, 10% were experienced within 21–25 years, and 16.5% of the respondents were experienced above 25 years. This reflected that most of the respondents had the requisite experience in working in the healthcare sector; the result of the study was ascertained adequate since those who had experience in managing constructed facilities in the healthcare sector were administered the questionnaire on an innovation (DT) for improving the maintenance management of public hospital buildings in Nigeria. Looking at Table 4, the age distribution of the respondents revealed that none of the respondents were between the age range of 21 and 30 years, 16.7% of them were between the range of 31 and 40 years, 50% were between 41 and 50 years, 23.3% were between 51 and 60 years, while 10% were above 60 years. This also reflected that the conclusion of the study was satisfactory since most of the respondents were within the age ranges that can adequately adopt a technological tool; in the process of time, these categories of respondents will be in active service as policymakers and implementers for the DTMM of constructed facilities in the healthcare sector. The result on the educational status of the respondents showed that none of the respondents filled the ordinary national diploma and the higher national diploma status, 37.1% attained the Bachelor of Science (B.Sc) degree, 16.5% attained the Bachelor of medicine and surgery (MB.BS), 33.5% attained the Master of Science (M.Sc) degree, 12.9% had attained the Doctor of Philosophy status. This shows that the respondents were qualified through experience, expertise, and training to give the relevant information needed for the study.

The results on the occupation distribution of the respondents outlined that the maintenance personnel (builders, architects, estate surveyors, quantity surveyors, and engineers) amounted to 60.1% of the total respondents used for the analysis; the medical/dental doctors and other medical allied specialists (medical laboratory scientists, radiologists, pharmacists, microbiologists, optometrists, to mention) amounted to 29.9%, the nurses amounted to 3.4%, and the administrators 6.6%. This means that the relevant professionals were represented in the survey. From Tables 4, it was seen that all the respondents were affiliated to their respective professional bodies which are the Council of Registered Builders of Nigeria, Architects Registration Council of Nigeria, Estate Surveyors and Valuers Registration Board of Nigeria (ESVARBON), Quantity Surveyors Registration Board of Nigeria (QSVRBON), Council of Registered Engineers of Nigeria (COREN), Medical and Dental Council of Nigeria (MDCN), Nursing and Midwifery Council of Nigeria (NMCN), and Others like Pharmacists Council of Nigeria (PCN), Medical Laboratory Scientists Council of Nigeria (MLSCN), Optometrists and Dispensing Opticians Registration Board of Nigeria (ODORBN), Institute of Chartered Accountants of Nigeria (ICAN), and Institute of Health Services Administrators of Nigeria (IHSAN), to mention. This implied that all the respondents were professionally certified and registered and not quacks, which influenced the needed outcome of the study. The results of the respondents in this study revealed that 9.7% were among the top management staff of public hospitals in Nigeria, 33.3% were heads of departments and directors, and 57% were maintenance personnel. This portrayed that the respondents needed for the study were proportionally captured, as it is normal for a few persons to ascend to the top management level, and the maintenance personnel have been highest to meet the needs of this study adequately.

A unidimensional model for DTMM drivers is presented in this section. A total of 442 cases were examined to analyse the drivers of DTMM. Though the first model had 17 observed variables, seven indicator variables from the preliminary CFA were removed. For a variable to be included in a CFA and for the model to be said to be well-fitting, the residual covariance matrix distribution should be symmetrical and centred around zero, according to Joreskog and Sorbom (1988) and Byrne (2006:94), while a residual covariance matrix value greater than 2.58 is considered to be extensive (Byrne, 2006, p. 94; Joreskog and Sorbom, 1988; Aigbavboa, 2014). The remaining ten-indicator model, which included DTMO2, DTMO4, DTMO5, DTMO6, DTMO10, DTMO11, DTMO12, DTMO13, DTMO15, and DTMO17, was therefore retained since it offered a good indication of the residual matrix and proof of convergent validity. This is given in Figure 2.

The ten dependent indicator variables for DTMM drivers were smart management of site activities, increased collection of real-time information on buildings, prediction of building component failures, maintenance cost reduction due to prediction before failures, increased reliability of building components, increased level of integration among building components, increased digitalisation of maintenance activities, increased information and drive for sustainability efforts, increased scheduled maintenance from predictions, and remote troubleshooting regardless of geographical location. Table 5 displays the indicator variables. To determine how well the model fits the sample data and the potency of the hypothesised relationship between the variables, the residual covariance matrix (standardised and unstandardised), distribution of standardised residuals, fit statistics, and statistical significance at a probability level of 5% were examined. The dependability of the scores was further evaluated using Cronbach’s alpha and the rho coefficient of internal consistency. The next component of the DTMM driver model presents the findings of these statistics.

Tables 6 and 7 illustrate the DTMM drivers’ unstandardised and standardised absolute residual matrix values. The findings show that both the absolute residual values and the average off-diagonal residual values were nearly zero. Compared to the unstandardised average off-diagonal residual, which was 0.0013, the standardised average off-diagonal residual was determined to be 0.0063. According to Byrne (2006:94) and Aigbavboa (2014:406), the distribution of standardised residuals should be symmetrical and centred around zero for a model to be described as well-fitting, whereas a residual value greater than 2.58 is described as large (Byrne, 2006, p. 94; Aigbavboa, 2014, p. 406). As a result, the model could be said to be well-fitting. Because all the absolute residuals were smaller than 2.58, the results of the DTMM Drivers Measurement Model revealed a good fit to the sample data. The model could also be characterised as being well-fitting.

A subsequent frequency distribution analysis showed that all the residual values (100%) fell between −0.1 and + 0.1, within the permissible range. The findings indicated that the DTMM drivers’ measurement model was well-fitting despite a minor difference between the hypothesised model and the sample data. It was, therefore, possible to make a firm judgement regarding the suitability and fit of the measurement model, thanks to additional goodness-of-fit tests, because this diagnostic fit analysis revealed a satisfactory fit.

Following a two-statistics fit index technique, the analytical strategy of goodness-of-fit for DTMM drivers followed Hu and Bentler's (1999) recommendation. The Satorra-Bentler scaled chi-square (S-Bχ²) of 27.416 with 35 degrees of freedom (df) was obtained from the sample data on the DTMM drivers’ measurement model. The chi-square was, therefore, not significant. This chi-square result suggests that the sample data’s fit to the measurement model was reasonable because the sample data’s divergence from the model was not statistically significant. The chi-square test, however, is very sensitive to sample size, and Kline (2005:136) claimed that its use is more akin to a descriptive measure of fit than a statistical test. As a result, most studies utilise the normed chi-square (χ²/df) value (Kline, 2005; Musonda, 2012; Agumba, 2013; Aigbavboa, 2014). The normed chi-square value can be calculated by dividing the chi-square by the degrees of freedom. It is recommended to use normed values lower than 3.0 or even 5.0 (Kline, 2005, p. 137). The ratio was, therefore, determined to be 0.783 based on the degrees of freedom and chi-square values. This ratio provided a good fit model because it was below the 3.00 or 5.0 limit suggested by other authors (Kline, 2005, p. 137). This is displayed in Table 8.

Additionally, the Comparative Fit Index was discovered to be 0.991, which is higher than the cut-off value of 0.95 and suggests a model that has a good fit. Also discovered to be 0.069 was the root mean square error of approximation. This is below the threshold value of 0.08, indicating a reasonably good model that fits the data. These fit indices for the DTMM drivers’ measurement model suggest that the assumed model sufficiently represents the sample data and can thus be used in the full latent variable model analysis.

Prior to drawing any conclusions about the suitability of the postulated models, Musonda (2012) and Raykov et al. (1991, p. 501) suggested that factor loadings (parameter coefficients), standard errors, and the test statistics be further examined. Considering this claim, Table 9’s correlation values, standard errors, and test statistics were examined. All the correlation values were not greater than 1, the Z-statistics were higher than 1.96, and the signs were all suitable. The estimates were deemed plausible and statistically significant. DTMO10 (Increased reliability of building components) was used as the indicator variable with the greatest standardised coefficient. 0.988 was discovered to be the parameter coefficient.

Even though all parameter estimates showed good correlations with values near 1.00, it was discovered that the variable DTMO13 had the strongest association with DTMM. The high correlation values imply that the indicator and unobserved variables (DTMM) have a strong linear relationship. Further evidence that the factors explained a more significant proportion of the variance in the indicator variables came from the R² values, which were near the intended value of 1.00. The findings indicate a significant predictive relationship between the indicator variables and the unobserved construct. DTMM is significantly correlated with all the assessed factors.

The rho and Cronbach’s alpha coefficients were used to assess scores’ internal consistency and reliability for the DTMM drivers’ results. The dependability coefficient should be between 0 and 1.00, while values near 1.00 are preferred, according to Kline (2005:59). It was discovered that the rho coefficient of internal consistency was 0.993. This result exceeded the 0.70 threshold, which is the absolute minimum. The Cronbach’s alpha was also above the minimum allowable level of 0.70. The Cronbach’s alpha was found to be 0.905 (Table 10). The rho and Cronbach Alpha values showed high internal consistency and reliability levels, indicating that the indicator variables represent the same latent construct (DTMM drivers).

The CFA showed that the residual covariance estimates were within a reasonable range. Additionally, all parameter estimates were statistically significant and practicable, and the robust fit indices all met the cut-off index criteria. Considering these criteria, it was determined that the measurement model for the DTMM drivers adequately matched the sample data. Therefore, there was no need to enhance the measurement model, and the ten (10) indicator variables provided sufficient measurement of the drivers of DTMM for healthcare facilities in Nigeria.

The study on DTMM drivers for hospital facilities in Nigeria revealed that direct and indirect influences of the latent variables would manifest in DTMM drivers. The endogenous variable was made up of ten latent variables. The ten latent variables of DTMM drivers were smart management of site activities, increased collection of real-time information on buildings, prediction of building component failures, maintenance cost reduction due to prediction before failures, increased reliability of building components, increased level of integration among building components, increased digitalisation of maintenance activities, increased information and drive for sustainability efforts, increased scheduled maintenance from predictions, and remote troubleshooting regardless of geographical location. The latent variable that contributed the most to hospital facilities’ DTMM was the increased reliability of building components. This outcome revealed the expectations from the adoption of DT technology for the maintenance management of hospital facilities in Nigeria and other developing countries. Conversely, integration among building components contributed least to the DTMM outcomes, suggesting that greater emphasis on interoperability and system integration could further optimise DT implementation. These results corroborate previous research by Madni et al. (2019), Khajavi et al. (2019), Angjeliu et al. (2020), Opoku et al. (2021, 2022), and Madubuike and Anumba (2023), which highlight the significance of component reliability and integration within DT frameworks.

The information advocated by this study will prompt the management of public hospitals to provide germane training packages, which will develop the capacity of maintenance personnel to enhance their information technology (IT) maturity and use of Industry 4.0 tools. The need for maintenance documentation, which significantly influences DTMM (Ebiloma et al., 2023), will prompt the need to train professionals on producing as-built models, early warning system charts, activity schedule plans, and other required DTMM documents. This leads to further capacity development on Industry 4.0 tools like sensors and actuators, machine learning, BIM, cloud computing, and the Internet of Things. The focus on maintenance documentation by the personnel and stakeholders will improve their IT maturity and tend towards using Industry 4.0 tools to attain DTMM of constructed facilities. More specifically, the outcome of this study is a critical coordinate on the roadmap to fostering the adoption of DT technology in the Nigerian construction industry. This study affirms that addressing key DTMM drivers in the Nigerian healthcare sector can substantially enhance facility management efficiency. The full adoption and implementation of DT technology promises to transform healthcare facility maintenance into a more proactive, efficient, and sustainable process. There is a significant relationship between the issues surrounding constructed facilities in the Nigerian healthcare sector and the drivers of DTMM. This implies that the management of constructed facilities in the healthcare sector will become efficient and intelligent when DT technology is fully adopted and implemented.

This study employed the Delphi study and the SEM technique to evaluate the key drivers of DTMM for hospital facilities in Nigeria. The findings identified ten essential DTMM drivers applicable to healthcare facilities in Nigeria. The ten variables of DTMM drivers were smart management of site activities, increased collection of real-time information on buildings, prediction of building component failures, maintenance cost reduction due to prediction before failures, increased reliability of building components, increased level of integration among building components, increased digitalisation of maintenance activities, increased information and drive for sustainability efforts, increased scheduled maintenance from predictions, and remote troubleshooting regardless of geographical location. The gap in the dearth of a study that provides the motivating factors for adopting DT technology for managing healthcare facilities in Nigeria has been filled with this study. These insights provide critical information for healthcare authorities and management organisations, highlighting the benefits and relevance of DT technology in facility maintenance. By informing policy and guiding decision-making, this study supports the broader implementation of DT technology in healthcare infrastructure management. Training maintenance personnel in DT applications within healthcare institutions is essential to realise these benefits. Furthermore, this research lays the foundation for future studies to refine and enhance the model, tailoring indicator variables to specific operational contexts. This study has produced valuable and intriguing results, but it is not without limitations. The following restrictions on the current study should be considered. First off, the study was limited to Nigeria. This is because of an increased focus on Nigeria’s healthcare infrastructure. If funds were available, conducting a similar research study on all the public hospital facilities in developing nations would be desirable. Given sufficient funding, it would be desirable to carry out a similar research study on a larger population.

This paper forms part of a special section “Beyond BIM – Navigating the Transformative Journey of the AECO Industry”, guest edited by Mehran Oraee, M. Reza Hosseini and Farzad Rahimian.